Method and apparatus for dynamically selecting content for online visitors

ABSTRACT

A computer-implemented method and an apparatus dynamically select content for online visitors. The method includes receiving information related to activity of an online visitor on an enterprise interaction channel and identifying channel data related to the activity. A plurality of content pieces capable of being provided to the online visitor during the ongoing journey is identified. A correlation score is computed for each content piece using the channel data to generate a plurality of correlation scores. The plurality of content pieces are rank-ordered by sorting the plurality of correlation scores. A display of at least one content piece is effected during the ongoing journey of the online visitor on the enterprise interaction channel based on the rank-ordering of the plurality of content pieces.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional patent applicationSer. No. 62/255,252, filed Nov. 13, 2015, which is incorporated hereinin its entirety by this reference thereto.

TECHNICAL FIELD

The present technology generally relates to content display mechanismsfor displaying content to customers on enterprise interaction channelsand more particularly to a method and apparatus for dynamicallyselecting content for online visitors and displaying the selectedcontent to the online visitors during their respective online journeys.

BACKGROUND

Many enterprises aim to offer content that may be of interest to theircustomers to improve chances of a sale or to provide an enrichedcustomer experience. The content is typically offered to existing andpotential customers of the enterprise on enterprise interactionchannels, such as Websites, native mobile applications, social media,and the like. The existing or potential customers of the enterprisevisiting such enterprise interaction channels are hereinafter referredto as online visitors.

Typically, the online visitor population is categorized into segmentsbased on age, gender, professional activity, location, etc., tofacilitate selection of content to be provided to the online visitorsduring their visit to the enterprise interaction channels. Selection ofcontent based on such a categorization of the online visitors hasseveral shortcomings. For example, the selected content may not becustomized to an individual online visitor's current behavior orpreference. In many scenarios, the content provisioned to an onlinevisitor is not what the online visitor may be interested in for hiscurrent visit to the enterprise interaction channel, and the onlinevisitor may ignore such content. In some cases, the online visitor mayalso get frustrated from viewing irrelevant content and may exit theinteraction, leading to a loss for the enterprise.

There is a need to provision content that is customized to an individualonline visitor's current behavior or preference. Moreover, there is aneed to extend such a provisioning of relevant content to scenarios,where sufficient historical visitor interaction data is absent.

SUMMARY

In an embodiment of the invention, a computer-implemented method fordynamically selecting content for online visitors is disclosed. Themethod receives, by a processor, information related to activity of anonline visitor on an enterprise interaction channel for an ongoingjourney of the online visitor on the enterprise interaction channel. Themethod identifies, by the processor, channel data related to theactivity of the online visitor using the received information. Thechannel data includes one or more data segments. For a plurality ofcontent pieces capable of being provided to the online visitor duringthe ongoing journey on the enterprise interaction channel, the methodcomputes, by the processor, a correlation score for each content piecefrom among the plurality of content pieces using the channel data. Thecomputation of the correlation score for each content piece generates aplurality of correlation scores. The method rank-orders, by theprocessor, the plurality of content pieces by sorting the plurality ofcorrelation scores. The method effects, by the processor, a display ofat least one content piece from among the plurality of content piecesduring the ongoing journey of the online visitor on the enterpriseinteraction channel. The display of the at least one content piece iseffected based on the rank-ordering of the plurality of content pieces.

In another embodiment of the invention, an apparatus for dynamicallyselecting content for online visitors includes at least one processorand a memory. The memory stores machine executable instructions thereinthat, when executed by the at least one processor, cause the apparatusto receive information related to activity of an online visitor on anenterprise interaction channel for an ongoing journey of the onlinevisitor on the enterprise interaction channel. The apparatus identifieschannel data related to the activity of the online visitor using thereceived information. The channel data includes one or more datasegments. For a plurality of content pieces capable of being provided tothe online visitor during the ongoing journey on the enterpriseinteraction channel, the apparatus is caused to compute a correlationscore for each content piece from among the plurality of content piecesusing the channel data. The computation of the correlation score foreach content piece generates a plurality of correlation scores. Theapparatus is caused to rank-order the plurality of content pieces bysorting the plurality of correlation scores. The apparatus is furthercaused to effect display of at least one content piece from among theplurality of content pieces during the ongoing journey of the onlinevisitor on the enterprise interaction channel. The display of the atleast one content piece is effected based on the rank-ordering of theplurality of content pieces.

In another embodiment of the invention, an apparatus for dynamicallyselecting content for online visitors includes at least onecommunication interface configured to receive information related toactivity of an online visitor on an enterprise Website for an ongoingjourney of the online visitor on the enterprise Website. The apparatusfurther includes at least one processor and a memory. The memory storesmachine executable instructions therein that, when executed by the atleast one processor, cause the apparatus to identify Website datarelated to the activity of the online visitor using the receivedinformation. The Website data includes data corresponding to one or moreWeb pages visited by the online visitor during the ongoing journey. Fora plurality of widgets capable of being provided to the online visitorduring the ongoing journey on the enterprise Website, the apparatus iscaused to compute a correlation score for each widget from among theplurality of widgets using the Website data. The computation of thecorrelation score for each widget generates a plurality of correlationscores. The apparatus is caused to rank-order the plurality of widgetsby sorting the plurality of correlation scores. The apparatus is furthercaused to effect display of at least one widget from among the pluralityof widgets during the ongoing journey of the online visitor on theenterprise Website. The display of the at least one widget is effectedbased on the rank-ordering of the plurality of widgets.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a representation illustrating an example interactionbetween a customer and an enterprise, in accordance with an examplescenario.

FIG. 2 is a block diagram of an apparatus configured to dynamicallyselect content for online visitors, in accordance with an embodiment ofthe invention;

FIG. 3 shows an example representation for illustrating a dynamic widgetselection problem, in accordance with an embodiment of the invention;

FIG. 4 shows a graphical representation for illustrating a computationof a page-wise cosine similarity score, in accordance with an embodimentof the invention;

FIG. 5 shows an example representation for illustrating a process flowfor selection of top k widgets, in accordance with an embodiment of theinvention;

FIG. 6 shows an example representation for illustrating a process flowfor selection of top k widgets, in accordance with another embodiment ofthe invention;

FIG. 7 shows an example representation for illustrating a provisioning aportion of Web page content as an input to an ESN reservoir, inaccordance with an embodiment of the invention;

FIG. 8 is an example flow diagram of a method for dynamically selectingcontent for an online visitor, in accordance with an embodiment of theinvention; and

FIG. 9 is an example flow diagram of a method for dynamically selectingcontent for an online visitor, in accordance with another embodiment ofthe invention.

DETAILED DESCRIPTION

The detailed description provided below in connection with the appendeddrawings is intended as a description of the present examples and is notintended to represent the only forms in which the invention hereindisclosed may be constructed or used. However, the same or equivalentfunctions and sequences may be accomplished by different examples.

FIG. 1 shows a representation 100 for illustrating an exampleinteraction between a customer and an enterprise, in accordance with anexample scenario. More specifically, the representation 100 depicts acustomer 102 (hereinafter referred to as an online visitor 102)accessing a Website 104 using a Web browser application 106 installed ona desktop computer 108. The Website 104 may be hosted on a remote Webserver and the Web browser application 106 may be configured to retrieveone or more Web pages associated with the Website 104 from the remoteWeb server over a communication network (not shown in FIG. 1). Examplesof the communication network may include wired networks, wirelessnetworks, or a combination thereof. Some examples of the wired networksmay include Ethernet, local area network (LAN), fiber-optic cablenetwork, and the like. Some examples of the wireless networks mayinclude cellular networks like GSM/3G/4G/CDMA networks, wireless LAN,blue-tooth or ZigBee networks, and the like. An example of thecombination of wired and wireless networks may include the Internet. Itis understood that the Website 104 may attract a large number ofexisting and potential customers, such as the online visitor 102.

In the representation 100, the Website 104 is exemplarily depicted to bean e-commerce Website displaying a variety of products and services forsale to online visitors during their journey on the Website 104. It isnoted that the term ‘journey’ as used throughout the description refersto a path an online visitor may take to reach a goal when using aparticular interaction channel. For example, the online visitor'sjourney on the Website 104 may include a number of Web page visits anddecision points that carry the online interaction of the online visitorfrom one step to another step. In an example scenario, the onlinevisitor 102 may be interested in purchasing a laptop and may visit theWebsite 104 to view laptops offered for sale by various vendors on theWebsite 104 and evaluate options for purchasing the laptop. Typically,the enterprise may display content, which is generalized for frequentvisitors to the Website 104. More specifically, the content may not becustomized to individual preferences of the online visitors, such as theonline visitor 102. For example, a widget, such as a widget 110,including content related to a promotional offer on purchase offurniture may be displayed to the online visitor 102 during the onlinevisitor's journey on the Website 104.

In another illustrative example one or more advertisements, such as anadvertisement 112 related to an on-going sale on apparel, may bedisplayed to the online visitor 102. The online visitor 102 wishing topurchase a laptop may ignore such content being offered on the Website104. In many cases, the online visitor 102 on account of having to siftthrough large amount of uninteresting information to identify content ofinterest, may get frustrated and may exit the interaction, leading to aloss for the enterprise.

Some enterprises may use historical interaction data associated with theonline visitors, such as previous session data of the online visitors onthe Website 104, to predict intentions of the online visitors. Forexample, the historical interaction data of the online visitor 102 mayindicate the online visitor 102 being interested in a particular brandof laptops. Accordingly, relevant content and/or information (forexample, offers on laptops of a desired brand) may be displayed to theonline visitor 102 on the Website 104 to increase chances of a sale orto provide an improved browsing experience to the online visitor 102.However, in many cases, sufficient historical interaction data may notbe available for accurately predicting intentions of some onlinevisitors. For example, an online visitor may be visiting an enterpriseinteraction channel for the first time. In such a case, the historicalinteraction data may not be available for facilitating prediction of theonline visitor's intention and as such, a provisioning of appropriatecontent to such an online visitor may be a challenge. Moreover, afirst-time visitor may not enjoy viewing content that is not found to beof interest and may exit the interaction, perhaps never to return. Suchnegative online visitor experiences are detrimental to enterpriseobjectives.

Various embodiments of the invention provide a method and apparatus thatare capable of overcoming these and other obstacles and providingadditional benefits. More specifically, various embodiments of theinvention disclosed herein present unsupervised learning basedapproaches that can be used to dynamically determine relevant contentbased on an individual visitor's journey across an enterpriseinteraction channel. Further, the dynamic selection of relevant contentmay be extended to scenarios, where sufficient historical visitorinteraction data is absent. An apparatus for dynamically selectingcontent for online visitors is explained with reference to FIG. 2.

FIG. 2 is a block diagram of an apparatus 200 configured to dynamicallyselect content for online visitors in accordance with an embodiment ofthe invention. It is noted that the term ‘online visitor’ as used hereinrefers to a customer (for example, an existing customer or a potentialcustomer of an enterprise) visiting an enterprise interaction channel,such as for example, an enterprise Website, an enterprise native mobileapplication, or an enterprise social media portal. Moreover, the term‘enterprise’ as used herein may refer to a corporation, an institution,a small/medium sized company, or even a brick and mortar entity. Forexample, the enterprise may be a banking enterprise, an educationalinstitution, a financial trading enterprise, an aviation company, aconsumer goods enterprise, or any such public or private sectorenterprise.

Furthermore, the term ‘dynamically selecting content’ as used hereinrefers to selecting at least one content piece from among a plurality ofcontent pieces such as widgets, advertisements, news stories, pop-upsoffering agent assistance, and the like, in substantially real-timebased, at least in part, on the current journey of the online visitor onan enterprise interaction channel. The selected content pieces may bethen be provisioned, i.e. displayed to the online visitor during theongoing journey of the online visitor on the enterprise interactionchannel, as will be explained hereinafter.

The apparatus 200 includes at least one processor, such as a processor202 and a memory 204. It is noted that although the apparatus 200 isdepicted to include only one processor, the apparatus 200 may includeany number of processors therein. In an embodiment, the memory 204 iscapable of storing machine executable instructions, referred to hereinas platform instructions 205. Further, the processor 202 is capable ofexecuting the platform instructions 205. In an embodiment, the processor202 may be embodied as a multi-core processor, a single core processor,or a combination of one or more multi-core processors and one or moresingle core processors. For example, the processor 202 may be embodiedas one or more of various processing devices, such as a coprocessor, amicroprocessor, a controller, a digital signal processor (DSP), aprocessing circuitry with or without an accompanying DSP, or variousother processing devices including integrated circuits such as, forexample, an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a microcontroller unit (MCU), a hardwareaccelerator, a special-purpose computer chip, or the like. In anembodiment, the processor 202 may be configured to execute hard-codedfunctionality. In an embodiment, the processor 202 is embodied as anexecutor of software instructions, wherein the instructions mayspecifically configure the processor 202 to perform the algorithmsand/or operations described herein when the instructions are executed.

The memory 204 may be embodied as one or more volatile memory devices,one or more non-volatile memory devices, and/or a combination of one ormore volatile memory devices and non-volatile memory devices. Forexample, the memory 204 may be embodied as magnetic storage devices(such as hard disk drives, floppy disks, magnetic tapes, etc.), opticalmagnetic storage devices (e.g. magneto-optical disks), CD-ROM (compactdisc read only memory), CD-R (compact disc recordable), CD-R/W (compactdisc rewritable), DVD (Digital Versatile Disc), BD (BLU-RAY® Disc), andsemiconductor memories (such as mask ROM, PROM (programmable ROM), EPROM(erasable PROM), flash memory, RAM (random access memory), etc.).

The apparatus 200 also includes an input/output module 206 (hereinafterreferred to as ‘I/O module 206’) and at least one communicationinterface such as the communication interface 208. In an embodiment, theI/O module 206 may include mechanisms configured to receive inputs fromand provide outputs to the user of the apparatus 200. To that effect,the I/O module 206 may include at least one input interface and/or atleast one output interface. Examples of the input interface may include,but are not limited to, a keyboard, a mouse, a joystick, a keypad, atouch screen, soft keys, a microphone, and the like. Examples of theoutput interface may include, but are not limited to, a display such asa light emitting diode display, a thin-film transistor (TFT) display, aliquid crystal display, an active-matrix organic light-emitting diode(AMOLED) display, a microphone, a speaker, a ringer, a vibrator, and thelike.

In an example embodiment, the processor 202 may include I/O circuitryconfigured to control at least some functions of one or more elements ofthe I/O module 206, such as, for example, a speaker, a microphone, adisplay, and/or the like. The processor 202 and/or the I/O circuitry maybe configured to control one or more functions of the one or moreelements of the I/O module 206 through computer program instructions,for example, software and/or firmware, stored on a memory, for example,the memory 204, and/or the like, accessible to the processor 202.

The communication interface 208 may include several channel interfacesto communicate with a plurality of enterprise interaction channels. Somenon-limiting examples of the enterprise interaction channels may includea Web channel (i.e. an enterprise Website), a voice channel (i.e.voice-based customer support), a chat channel (i.e. a chat support), anative mobile application channel, a social media channel and the like.Each channel interface may be associated with a respective communicationcircuitry such as for example, a transceiver circuitry including antennaand other communication media interfaces to connect to a wired and/orwireless communication network. The communication circuitry associatedwith each channel interface may, in at least some example embodiments,enable transmission of data signals and/or reception of signals fromremote network entities, such as Web servers hosting enterprise Websiteor a server at a customer support or service center configured tomaintain real-time information related to interactions between customersand agents.

In at least one example embodiment, the channel interfaces areconfigured to receive up-to-date information related to thecustomer-enterprise interactions from the enterprise interactionchannels. In some embodiments, the information may also be collated fromthe plurality of devices used by the customers. To that effect, thecommunication interface 208 may be in operative communication withvarious customer touch points, such as electronic devices associatedwith the customers, Websites visited by the customers, devices used bycustomer support representatives (for example, voice agents, chatagents, IVR systems, in-store agents, and the like) engaged by thecustomers, and the like.

The communication interface 208 may further be configured to receiveinformation related to current journeys of online visitors on enterpriseinteraction channels, such as enterprise Websites, enterprise nativemobile applications, enterprise social media forums, etc., in real-timeand provide the information to the processor 202. In at least someembodiments, the communication interface 208 may include relevantapplication programming interfaces (APIs) to communicate with remotedata gathering servers associated with such enterprise interactionchannels. Moreover, the communication between the communicationinterface 208 and the remote data gathering servers may be realized overvarious types of wired or wireless networks.

In an embodiment, various components of the apparatus 200, such as theprocessor 202, the memory 204, the I/O module 206 and the communicationinterface 208 are configured to communicate with each other via orthrough a centralized circuit system 210. The centralized circuit system210 may be various devices configured to, among other things, provide orenable communication between the components (202-208) of the apparatus200. In certain embodiments, the centralized circuit system 210 may be acentral printed circuit board (PCB) such as a motherboard, a main board,a system board, or a logic board. The centralized circuit system 210 mayalso, or alternatively, include other printed circuit assemblies (PCAs)or communication channel media.

It is noted that the apparatus 200 as illustrated and hereinafterdescribed is merely illustrative of an apparatus that could benefit fromembodiments of the invention and, therefore, should not be taken tolimit the scope of the invention. It is noted that the apparatus 200 mayinclude fewer or more components than those depicted in FIG. 2. In anembodiment, the apparatus 200 may be implemented as a platform includinga mix of existing open systems, proprietary systems, and third partysystems. In another embodiment, the apparatus 200 may be implementedcompletely as a platform including a set of software layers on top ofexisting hardware systems. In an embodiment, one or more components ofthe apparatus 200 may be deployed in a Web Server. In anotherembodiment, the apparatus 200 may be a standalone component in a remotemachine connected to a communication network and capable of executing aset of instructions (sequential and/or otherwise) to dynamically selectcontent to be provisioned to online visitors based on their respectivejourneys on the enterprise interaction channels. Moreover, the apparatus200 may be implemented as a centralized system, or, alternatively, thevarious components of the apparatus 200 may be deployed in a distributedmanner while being operatively coupled to each other. In an embodiment,one or more functionalities of the apparatus 200 may also be embodied asa client within devices, such as customers' devices. In anotherembodiment, the apparatus 200 may be a central system that is shared byor accessible to each of such devices.

The dynamic selection of content by the apparatus 200 is hereinafterexplained with reference to one online visitor. It is noted theapparatus 200 may be caused to dynamically select content for severalonline visitors in a similar manner.

In at least one example embodiment, the processor 202 is configured to,with the content of the memory 204, cause the apparatus 200 to receiveinformation related to activity of an online visitor on an enterpriseinteraction channel for an ongoing journey of the online visitor on theenterprise interaction channel. As explained above, the communicationinterface 208 is configured to receive information related to journeysof various online visitors on one or more enterprise interactionchannels in an ongoing manner in real time. Some non-exhaustive examplesof an enterprise interaction channel may include a Web channel, a socialchannel, a native application channel, and the like. In an illustrativeexample, an online visitor may visit a Website, i.e. the Web channelassociated with an enterprise for learning about products and/orservices associated with the enterprise. In such a scenario, thecommunication interface 208 may be configured to receive informationrelated to activity of the online visitor, such as information relatedto a current Web page that the online visitor is accessing, previous Webpages visited, time spent on a Web page, search terms used, and thelike. The communication interface 208 may be configured to send thereceived information to the processor 202 directly, or to store theinformation in the memory 204 for subsequent access by the processor202.

In at least one example embodiment, the processor 202 is configured to,with the content of the memory 204, cause the apparatus 200 to identifychannel data related to the activity of the online visitor using thereceived information. For example, if the received informationcorresponds to online visitor's Website activity information, then theapparatus 200 may be caused to identify which Web pages the onlinevisitor has visited during the journey. In such a scenario, the channeldata identified for such a visitor journey may correspond to data (forexample, textual data) corresponding to Web page content of the Webpages visited by the online visitor during the ongoing journey on theenterprise Website.

In another illustrative example, if the received information correspondsto online visitor's activity on a native mobile application channel,then the apparatus 200 may be caused to identify textual data related tovarious Uls accessed by the online visitor during a journey on thenative mobile application as the channel data.

Channel data may include one or more data segments. For example, if thechannel data corresponds to overall content of all Web pages visited bythe online visitor, then each data segment may correspond to contentrelated to one Web page visited by the online visitor. Similarly, if thechannel data corresponds to overall textual content of Uls of the nativemobile application accessed by the online visitor, then each datasegment may correspond to textual content of one UI from among theseveral Uls accessed by the online visitor.

In at least one example embodiment, upon receiving information relatedto online visitor's journey, the processor 202 may be configuredretrieve all possible content (also interchangeably referred to hereinas ‘content pieces’) that may be offered to the online visitor from thememory 204. For example, the processor 202 may retrieve several contentpieces such as widgets (or pop-ups) including textual content related toadvertisements, promotional offers, discount coupons, news snippets,frequently asked questions (FAQs), and the like.

In at least one example embodiment, the processor 202 may be configuredto dynamically select appropriate content pieces from among theretrieved content pieces based on information received at any arbitrarypoint in time in the online visitor's journey and provision the selectedcontent pieces to the online visitor on the enterprise interactionchannel.

The dynamic selection of content pieces needs to be customized based onan individual online visitor's current journey. At a high level, thechallenge of dynamically selecting relevant content pieces may be statedas:

“At any precise time t, given a finite database of ‘m’ static contentpieces (for example, content pieces related to advertisements,promotional offers, discount coupons, news stories, frequently askedquestions (FAQ), and the like), how to determine ‘k’ relevant contentpieces, based on given interaction data for an arbitrary online visitoru, from time t−τ to time t, for time τ>0”.

The solution to such a problem is hereinafter explained using an exampleof content pieces in form of widgets. More specifically, the dynamicselection of content pieces by the apparatus 200 is explained using anexample of dynamic selection of widgets from a widget bank comprising aplurality of widgets. It is however noted that the dynamic selection ofcontent pieces may not be limited to widgets but may include any form ofcontent, such as those related to advertisements, news snippets,promotional offers, discount coupons, FAQs, etc., that may be processedto retrieve appropriate content for the online visitors. In at least oneexample embodiment, the widgets may include textual content related toadvertisements, news snippets, promotional offers, discount coupons,FAQs, and the like.

When stated in terms of widgets, the problem statement of dynamicselection of content assumes the form:

Given the following: (1) A widget bank, that constitutes a finite numberof m widgets, where each widget includes a small piece of text ofarbitrary length, that is relevant to a Website, for example, apromotional/marketing message targeting a specific product, and (2) anarbitrary length of Web journey (for example, n Web pages visited) bythe online visitor on the Website,

determine which of the top k widgets from the set of m widgets should beshown for any arbitrary point of time t (or for example at the i^(th)page load, where i=1, 2 . . . n).

A pictorial representation of the dynamic widget selection problem isdepicted in FIG. 3.

FIG. 3 shows an example representation 300 for illustrating a dynamicwidget selection problem, in accordance with an embodiment of theinvention. An online visitor at an arbitrary point of time during anongoing journey on a Website is shown to have visited a total of ‘n’ Webpages in the Website. The ‘n’ Web pages visited by the online visitor sofar are shown in the block 302 as Web pages ‘P₁’, ‘P₂’, ‘P₃’, ‘P₄’, ‘P₅’to ‘P_(n)’ in the example representation 300. In an illustrativescenario, the Website may be an e-commerce Website and the Web pages‘P₁’, ‘P₂’, ‘P₃’, ‘P₄’, ‘P₅’ to ‘P_(n)’, may correspond to a ‘homepage’, a ‘product page 1’, a ‘cart page’, a ‘checkout page’, a ‘returnproduct page’, and a ‘feedback page’, respectively.

As explained with reference to FIG. 2, the apparatus 200 is configuredto receive information (via the communication interface 208)corresponding to an ongoing journey of an online visitor. The apparatus200 is also associated with a widget bank 304 including a total of ‘m’widgets, ‘W₁’, ‘W₂’, ‘W₃’, ‘W₄’, ‘W₅’ to ‘W_(m)’ corresponding towidgets that may be offered to the online visitor upon the onlinevisitor's access of the Website. Although the widget bank 304 isdepicted to be externally stored with respect to the apparatus 200, inat least some embodiments, the memory 204 of the apparatus 200 maymaintain a local copy of the ‘m’ widgets constituting the widget bank304. In an illustrative example, the widgets ‘W₁’, ‘W₂’, ‘W₃’, ‘W₄’,‘W₅’ to ‘W_(m)’ may include static text content related to promotionalor marketing message targeting the relevant Web pages in the Website.

The dynamic widget selection problem in such a scenario corresponds tothe problem of determining the top ‘k’ relevant widgets from among ‘m’widgets, to be provided to the online visitor given the online visitor'sjourney encompassing a visit to Web pages from P₁ to P_(n).

Referring now to FIG. 2, in at least one example embodiment, theprocessor 202 of the apparatus 200 is configured to treat the channeldata as a strong indicator of ‘what the visitor is looking for’. Forexample, the apparatus may treat content of the Web pages that an onlinevisitor to a Website has visited, as a strong indicator of ‘what thevisitor is looking for’. In an illustrative example, given two widgetsW₁ and W₂, with W₁ including text: ‘Click here for 50% discount onSmartphones’, and W₂ including text ‘Not happy with what you purchased?Return it with a single click’, an online visitor having visited Webpages predominantly in the category of ‘Mobiles & Smartphones’ is moresusceptible to click on W₁, while another online visitor who is in the‘Customer Service’ section and looking at FAQs about return policies, ismore likely to click on W₂. Accordingly, based on the channel data, oneor more widgets including relevant text content may be provisioned tothe online visitor.

In at least one example embodiment, the processor 202 is configured to,with the content of the memory 204, cause the apparatus 200 to compute,using channel data, a correlation score for each content piece fromamong a plurality of content pieces capable of being provided to theonline visitor during the ongoing journey on the enterprise interactionchannel. As explained above, the plurality of content pieces may includecontent pieces, such as widgets or pop-ups, displaying content relatedto at least one of advertisements, promotional offers, discount coupons,offers for agent assistance, news stories, frequently asked questions(FAQs), and the like. The computation of the correlation score for eachcontent piece from among the plurality of content pieces generates aplurality of correlation scores. The computation of correlation scoresusing channel data is explained hereinafter.

In at least one example embodiment, the apparatus 200 is caused tocompute, for each content piece, a cosine similarity score correspondingto each data segment from among the one or more data segments togenerate one or more cosine similarity scores for each content piece. Asexplained above, channel data, such as for example, data related to Webpages visited by the online visitor may be identified based on thereceived information related to the activity of the online visitor.Accordingly, a cosine similarity score may be computed for eachcombination of a data segment (for example, a Web page) and a contentpiece (for example, a widget). Each computed cosine similarity score isindicative of a measure of correlation between content corresponding toa respective content piece and content corresponding to a data segment.

In an embodiment, computing a cosine similarity score for a combinationof a data segment and a content piece includes identifying a list ofelements common to the content of the respective content piece and thecontent of the data segment. In an illustrative example, elements maycorrespond to words included in the textual content of the data segmentand the content piece. Accordingly, a list of words, which are common toa data segment and the content piece, may be identified.

The apparatus 200 may further be caused to compute a first vector and asecond vector based on a number of occurrences of each element fromamong the list of elements in the content of the data segment and thecontent piece, respectively. A cosine component of an angle between thefirst vector and the second vector may then be determined to compute thecosine similarity score corresponding to a combination of one datasegment and one content piece. The cosine similarity scores may besimilarly computed for combinations of various data segments with eachcontent piece. Further, the apparatus 200 is caused to compute, for eachcontent piece, an average value of the respective cosine similarityscores, i.e. average of cosine similarity scores corresponding tocombinations of a content piece and each data segment configuring thechannel data. In at least one example embodiment, the average value ofthe respective cosine similarity scores for each content piece may beconsidered as the correlation score for each content piece. Thecomputation of the correlation score based on cosine similarity scoresis explained next with reference to an illustrative example.

In an illustrative example, an online visitor may currently be accessingone or more Web pages of an enterprise Website. The apparatus 200 mayreceive the information related to activity of the online visitorcorresponding to the ongoing journey of the online visitor. Theapparatus 200 may further be caused to determine a correlation betweenthe content of Web pages and content of ‘m’ widgets. To that effect, inone embodiment, the processor 202 is configured to, with the content ofthe memory 204, cause the apparatus 200 to compute a page-wise cosinesimilarity score based on content of each Web page visited so far andtext content of each widget stored in the widget bank.

An example computation of a page-wise cosine similarity score based oncontent of a Web page P_(a) and text content of a widget W_(b) isexplained hereinafter. In an embodiment, a list ‘A’ of ‘j’ commondictionary words present in content of the Web page ‘P_(a)’ and the textcontent of the widget ‘W_(b)’ is generated. In an illustrative example,the list A including ‘j’ words common to both the Web page ‘P_(a)’ andthe widget ‘W_(b)’ may be represented as depicted below:

A=<w₁, w₂, . . . w_(j)>

Thereafter, a first vector referred to herein as ‘a page term vector({right arrow over (P_(a))})’ may be computed, where an ‘i^(th)’ elementof the page term vector ({right arrow over (P_(a))}) is a number ofoccurrences of word ‘w_(i)’ in the Webpage ‘P_(a)’ for ‘i’ =1, 2 . . .j. Similarly, a second vector referred to herein as a ‘widget termvector ({right arrow over (W_(b))})’ may be computed, where an ‘i^(th)’element of the widget term vector ({right arrow over (W_(b))}) is anumber of occurrences of word ‘w_(i)’ in the widget ‘W_(b)’ for ‘i’=1, 2. . . j.

The page-wise cosine similarity score may then be computed by theprocessor 202 of the apparatus 200 using the equation (1) depictedbelow:

σ({right arrow over (W _(b))}, {right arrow over (P _(a))})=({rightarrow over (W _(b))}. {right arrow over (P _(a))})/(∥{right arrow over(W _(b))}∥.∥{right arrow over (P _(a))}∥)   (1)

As can be seen from equation (1), the page-wise cosine similarity scoreobtained by computing σ({right arrow over (W_(b))}, {right arrow over(P_(a))}) between the page term vector ({right arrow over (P_(a))}) andthe widget term vector ({right arrow over (W_(b))}), is simply thecosine of the angle between the page term vector ({right arrow over(P_(c))}) and the widget term vector ({right arrow over (W_(b))}). Thecomputation of the first term vector, i.e. the page term vector ({rightarrow over (P_(a))}) and the second term vector, i.e. the widget termvector ({right arrow over (W_(b))}) based on the number of occurrencesof common words in respective content of a data segment, i.e. a Webpage, and a content piece, i.e. a widget is explained with reference toan illustrative example in FIG. 4.

FIG. 4 shows a graphical representation 400 (hereinafter referred to asplot 400) for illustrating a computation of a page-wise cosinesimilarity score, in accordance with an embodiment of the invention. Asexplained above, the page-wise cosine similarity score is indicative ofa measure of correlation between content of a data segment, such as aWeb page ‘P_(a)’, and a content piece, such as a widget ‘W_(b)’.Further, as explained above, to compute the page-wise cosine similarityscore, a list of words common to the respective contents of the Web page‘P_(a)’ and the widget ‘W_(b)’ may be identified.

In a simplified example scenario, a list of words present in the textualcontent associated with the Web page ‘P_(a)’ may be Laptop, Mobile,Printer, Keyboard, and Headphones. Similarly, a list of words present inthe textual content associated with the widget ‘W_(b)’ may be Discount,Laptop, Checkout, Mobile, Charger, and Printer.

Accordingly, a list ‘A’ of all the common dictionary words present incontent of the Web page ‘P_(a)’ and the text content of the widget‘W_(b)’ may correspond to:

A=<Laptop, Mobile, Printer>

Further, a number of occurrences of each word in list A in textualcontent corresponding to the Web page ‘P_(a)’ and the Widget ‘W_(b)’ maybe determined. For example, the words ‘Laptop’, ‘Mobile’, and ‘Printer’may occur one time, two times, and three times, respectively in the Webpage ‘P_(a)’. Further, the words ‘Laptop’, ‘Mobile’, and ‘Printer’ mayoccur two times, three times, and four times, respectively in the Widget‘W_(b)’. The number of occurrences of each common word may be plotted tocompute the first vector and the second vector as depicted in the plot400. More specifically, the plot 400 includes an X-axis 402 representinglist of common words (i.e. List A) present in the Web page ‘P_(a)’ andthe widget ‘W_(b)’. More specifically, (1,0) corresponds to the firstcommon word, i.e. a Laptop; (2,0) corresponds to the second common wordi.e. Mobile; and (3,0) corresponds to the third common word ‘Printer’and a Y-axis 404 representing numerical values corresponding to thenumber of occurrences of each common word in the List A.

The number of occurrences for words ‘Laptop’, ‘Mobile’ and ‘Printer’ maybe plotted at 410, 412, 414, i.e. at co-ordinates (1, 1), (2, 2), and(3, 3), respectively, to reflect one time, two times, and three timesoccurrence of these words in the content of the Web page. A first vectoror the page-term vector (P_(a)) may be computed by joining theseco-ordinates with a single directed line 406.

The number of occurrences for words ‘Laptop’, ‘Mobile’, and ‘Printer’may be plotted at 410, 416, 418, i.e. co-ordinates (1, 1), (2, 3), and(3, 5), respectively, to reflect one time, three times, and five timesoccurrence of these words in the content of the Web page. A secondvector or a widget-term Vector ({right arrow over (W_(b))}) may becomputed by joining these co-ordinates with a single directed line 408.

A page-wise cosine similarity score may then be computed by measuringthe cosine of the angle ‘θ’ 450 between the page-term vector ({rightarrow over (P_(a))}) and widget-term vector ({right arrow over (W_(b))})or alternately by using the equation depicted below:

σ({right arrow over (W _(b))}, {right arrow over (P _(a))})=({rightarrow over (W _(b))}, {right arrow over (P _(a))})/(∥{right arrow over(W _(b))}∥.∥{right arrow over (P _(a))}∥)

In at least one example embodiment, page-wise cosine similarity scoresmay be computed by the processor 202 for all the Web pages in theWebsite, with all the widgets in the widget bank. More specifically, acorrelation score (i.e., page-wise cosine similarity score) betweenwords in a Web page and words in a widget may be computed for all theWeb pages and widgets. In an illustrative example, if the Website has atotal of ‘n’ pages, and the widget bank has ‘m’ widgets, then an ‘m×n’matrix of page-wise cosine similarity scores may be generated. Such a‘m×n’ matrix of page-wise cosine similarity scores is exemplarilydepicted in FIG. 5.

Referring now to FIG. 5, an example representation 500 for illustratinga process flow for selection of top k widgets is shown, in accordancewith an embodiment of the invention. More specifically, therepresentation depicts matrix 502 including a plurality of entries,where each entry represents a cosine similarity score computed for a Webpage from among ‘n’ Web pages of a Web site and a widget from among ‘m’widgets in a widget bank, in accordance with an embodiment of theinvention. For example, the entry σ({right arrow over (W₁)}, {rightarrow over (P₁)}) represents a cosine similarity score computed based oncontent of widget 1 and content of Web page 1. The cosine similarityscores σ({right arrow over (W₁)}, {right arrow over (P₂)}), σ({rightarrow over (W₁)}, {right arrow over (P₃)}) to σ({right arrow over (W₁)},{right arrow over (P_(n))}) are computed based on content of widget 1and content of Web pages 2, 3 to n, respectively. Similarly, the cosinesimilarity scores σ({right arrow over (W₂)}, {right arrow over (P₁)}),σ({right arrow over (W₂)}, {right arrow over (P₂)}) to σ({right arrowover (W₂)}, {right arrow over (P₂)}) are computed based on content ofwidget 2 and content of the Web pages 1, 2 to n, respectively, and soon, and so forth.

In at least one example embodiment, the processor 202 is furtherconfigured to compute average cosine similarity scores for the Web pagesvisited by the online visitor. For example, based on the visitor'sjourney encompassing visit to Web pages P₂, P₃ and P₇, a matrix 504 maybe configured with each entry in the matrix 504 representing averagecosine similarity score corresponding to a widget from among the ‘m’widgets. The average cosine similarity score for each widget may becomputed by averaging the individual cosine similarity scores betweenthe widget and the three Web pages (i.e. the Web pages P₂, P₃, and P₇)visited by the online visitor during the visitor's journey on theWebsite. For example, cosine similarity scores between a widget W₁ andeach of the pages P₂, P₃, and P₇ may be averaged to determine an averagecosine similarity score for widget W₁ as depicted by entry ‘σ({rightarrow over (W₁)}, {right arrow over (P₂)})+σ({right arrow over (W₁)},{right arrow over (P₃)})+σ({right arrow over (W₁)}, {right arrow over(P₇)}))/3’. Similarly, an average cosine similarity score for widget 2may be computed as depicted in entry ‘σ({right arrow over (W₂)}, {rightarrow over (P₂)})+σ({right arrow over (W₂)}, {right arrow over(P₃)})+σ({right arrow over (W₂)}, {right arrow over (P₇)}))/3’ and soon, and so forth.

Referring now to FIG. 2, in at least one example embodiment, theprocessor 202 is configured to, with the content of the memory 204,cause the apparatus 200 to rank-order the plurality of content pieces bysorting the plurality of correlation scores. Further, the apparatus 200may be caused to display at least one content piece from among theplurality of content pieces to the online visitor during the ongoingjourney on the enterprise interaction channel based on the rank-orderingof the plurality of content pieces. More specifically, the processor 202may cause the communication interface 208 of the apparatus 200 to senddata signals related to the selection of at least one content piece to aWeb server hosting the enterprise Website, which may then display theone or more content piece during the ongoing journey of the onlinevisitor on the enterprise interaction channel. For example, in FIG. 5,the processor 202 may perform a rank-ordering of widgets andselect/display highest ranking widgets to the online visitor asexemplarily depicted in block 506. For example, the processor 202 may beconfigured to select top ‘k’ widgets based on the rank associated witheach widget. In an illustrative example, the value of ‘k’ may be chosento be one. Accordingly, the top ranking widget may be displayed to thevisitor on the Website. For example, for an online visitor visiting aWebsite to purchase a laptop, a widget including static content relatedto an equated monthly installment (EMI) scheme for purchasing laptopsmay be displayed to the online visitor on the Website. Similarly, awidget including advertisement content related to a promotional offer orcontent related to a Web-link showcasing laptop brand comparisons,laptop model reviews and the like, may also be displayed to the onlinevisitor. It is noted the widget including such textual content may bedisplayed to the online visitor as shown using widget 110 (oradvertisement 112) in FIG. 1. In some embodiments, for an online visitorwishing to resolve a query, a chat widget offering a chat interactionwith a human agent or a virtual agent, or alternatively, frequentlyasked questions (FAQ) page content may be displayed to the onlinevisitor. It is understood that the value of ‘k’ for selecting k topwidgets is described herein to be ‘one’ for illustration purposes and itis understood that the value of k may be chosen to be any number greaterthan one, and the display provided to the online visitor may beappended/modified, accordingly.

In an embodiment, the apparatus 200 is caused to compute for eachcontent piece, a cumulative cosine similarity score indicative of ameasure of correlation between content corresponding to a respectivecontent piece and cumulative content corresponding to the one or moredata segments. In an embodiment, computing the cumulative cosinesimilarity score for a content piece includes identifying a list ofelements common to content of the content piece and the cumulativecontent corresponding to the one or more data segments. The apparatus200 is further caused to compute a cumulative term vector based on anumber of occurrences of each element from among the list of elements inthe cumulative content, and, a second vector based on a number ofoccurrences of each element in the content of the content piece. Theapparatus 200 is then caused to compute the cumulative cosine similarityscore, at least in part, by determining a cosine component of an anglebetween the cumulative term vector and the second vector. The respectivecumulative cosine similarity score computed for each content piece mayconfigure the correlation score for the each content piece. Thecomputation of the cumulative cosine similarity score for each contentpiece is explained with an illustrative example below.

In the illustrative example explained with reference to FIGS. 3, 4, and5, the processor 202 of the apparatus 200 determines a correlationbetween the content of each Web page and the content of the ‘m’ widgets.In a similar manner, in some embodiments, the apparatus 200 may becaused to compute correlation in form of cosine similarity scores basedon cumulative text content of Web pages visited so far by the onlinevisitor during an on-going journey on the Website, and the text contentof individual widgets. For example, for a visitor's journey involvingvisits to Web pages 2, 3 and 7, a computation of cumulative-page cosinesimilarity scores may first involve creating a cumulative dictionaryfrom all unique words on Web pages P₂, P₃ and P₇. Thereafter, a list Aincluding ‘j’ common words between the words in the cumulativedictionary and words in a widget W_(b) may be generated.

In an illustrative example, the list A may be represented as depictedbelow:

A=<w ₁ , w ₂ , . . . w _(j)>

Thereafter, a cumulative term vector ({right arrow over (C_(2,3,7))})may be computed, where an ‘i^(th)’ element of the cumulative term vector({right arrow over (C_(2,3,7))}) is a number of occurrences of word‘w_(i)’ in the cumulative dictionary Σ_(2,3,7) for ‘i’=1, 2 . . . j.Similarly, a second vector (also referred to herein as the widget termvector ({right arrow over (W_(b))}) may be computed, where an ‘i^(th)’element of the widget term vector ({right arrow over (W_(b))}) is anumber of occurrences of word ‘w_(i)’ in the widget ‘W_(b)’ for ‘i’=1, 2. . . j.

The cumulative cosine similarity score may then be computed by theprocessor 202 of the apparatus 200 using the equation (2) depictedbelow:

σ({right arrow over (W _(b))}, {right arrow over (C _(2,3,7))})=({rightarrow over (W _(b))}, {right arrow over (C _(2,3,7))})/(∥{right arrowover (W _(b))}∥.∥{right arrow over (C _(2,3,7))}∥)   (2)

Having computed the cumulative cosine similarity scores for thecumulative dictionary, with respect to each of the ‘m’ widgets in thewidget bank, the widgets are then rank-ordered on the basis on sortingtheir cumulative cosine similarity scores (for example sorting indescending order). Based on this rank-ordering, the top k widgets may beprovisioned to the online visitor as explained with reference to FIG. 6.

Referring now to FIG. 6, an example representation 600 of a process flowfor illustrating a selection of top k widgets is shown, in accordancewith another embodiment of the invention. More specifically, the examplerepresentation 600 depicts a matrix 602 with each entry in the matrix602 representing an individual widget's cumulative cosine similarityscore for the online visitor's journey to Web pages, P₂, P₃ and P₇. Forexample, σ({right arrow over (W₁)}, {right arrow over (C_(2,3,7))}),σ({right arrow over (W₂)}, {right arrow over (C_(2,3,7))}), σ({rightarrow over (W₃)}, {right arrow over (C_(2,3,7))}) to σ({right arrow over(W_(m))}, {right arrow over (C_(2,3,7))}) represent cumulative-pagecosine similarity scores for widgets 1, 2, 3 to m, respectively. Thewidgets may then be rank-ordered, for example ordered from highestranking widget to lowest ranking widget based on their respectivecumulative cosine similarity scores.

The processor 202 may then be configured to select top ‘k’ widgets basedon the rank associated with each widget as exemplarily depicted in block604. As explained with reference to FIG. 5, the value of ‘k’ may beexemplarily chosen to be one or any such number to facilitate display ofthe top ranking widgets to the online visitor on the Website.

As explained above, the correlation between content pieces and the datasegments of the enterprise interaction channels may be computed usingeither segment-wise or page-wise cosine similarity scores or cumulativecosine similarity scores. In some embodiments, the apparatus 200 may becaused to compute the correlation score for each content piece usingreservoir computing frameworks, such as a recurrent neural network (RNN)framework. In an embodiment, the recurrent neural network frameworkcorresponds to an Echo state network (ESN). It is noted that ESN inprinciple, is a supervised learning based computational framework for arecurrent neural network (RNN). Moreover, it is noted that a sparselyconnected RNN is one type of reservoir. Further, it is also understoodthat the reservoir includes a plurality of neurons and synapses, whereeach neuron may be a node configured to receive input units fromexternal sources, as well as from other neurons in the reservoir, andthe synapses are configured to pass information between the neuronsand/or between the inputs and the neurons. In an example embodiment, theneurons in the reservoir may be connected to each other in a randommanner or using a predetermined probability distribution function. Thereservoir's dynamic state at time x(t), in response to the externalinput u(t), is the high-dimensional non-linear projection of thelow-dimensional input space. The time-constants of the neurons and thesynapses in the reservoir endows the reservoir with fading memory, i.e.the dynamic state of the reservoir at time t, x(t), not only containsinformation about the current input u(t), but also information aboutprevious inputs in duration [t−τ, t], where i is the fading memorycapacity of the reservoir. Finally, the desired output signal isobtained by using a trainable linear combination of the recurrentnetwork's dynamical state (i.e., simple linear readouts can be trainedto predict target values y(t) from x(t)).

In one example embodiment, the computation of correlation scores foreach content piece, such as a widget, may be computed by providing asequence of elements (for example, words) configuring the content pieceto a ESN reservoir and reading outing the final state of the ESNreservoir. The content of data segments (for example, Web page content)may be fed to a similar ESN reservoir and the final state of the ESNreservoir recorded. The final states of the two ESN reservoirs may becompared and based on a measure of similarity (or dissimilarity), acorrelation score for the content piece may be determined. In at leastone embodiment, the content of data segments and/or content pieces maybe fed element-by-element. Further, each element may be provided as aninput character-by-character. In some embodiments, the input sequence ofelements provided to the ESN reservoir may be limited by predefinednumber of characters. In an illustrative example, a set comprising aplurality of elements including elements corresponding to the one ormore data segments and elements corresponding to the plurality ofcontent pieces may be generated by the apparatus 200. A standarddeviation value and a mean value may be computed from respective lengthsof the plurality of elements. The apparatus 200 is further caused todetermine a length of each element in the inputs provided to the ESNreservoirs based on the standard deviation value and the mean value. Theoperations involved in computation of correlation scores using ESNreservoirs are further explained below.

In an embodiment, the apparatus 200 is caused to generate a primary ESNreservoir. As explained above, an ESN reservoir includes a plurality ofneurons, where one or more neurons are interconnected randomly orconnected to each other based on a predefined probability distributionfunction. The apparatus 200 is caused to provide a first inputcomprising a sequence of elements (for example, textual words)configuring the content of one or more data segments. The apparatus 200is further configured to record a state of the primary ESN reservoirsubsequent to provisioning of the first input to the primary ESNreservoir.

The apparatus 200 is further caused to generate a plurality of secondaryESN reservoirs. Each secondary ESN reservoir is configured to be a cloneof the primary ESN reservoir. More specifically, the neuron networkwithin each secondary ESN reservoir is configured to be similar to theneuron network within the primary ESN reservoir.

The apparatus 200 is caused to provide to each secondary ESN reservoir,a respective second input comprising a sequence of elements configuringthe content of one content piece from among the plurality of contentpieces.

A final state of each secondary ESN reservoir is recorded subsequent toprovisioning of the respective second input. The final state of eachsecondary ESN reservoir is compared to the state of the primary ESNreservoir to compute the correlation score for each content piece. Morespecifically, the correlation score for each content piece is computedbased on the comparison of the final state of the respective secondaryESN reservoir with the state of the primary ESN reservoir.

The computation of correlation score using ESN reservoirs is explainedwith reference to the earlier example of online visitor's ongoingjourney on an enterprise Website below.

To determine correlation between the content of Web pages and content ofthe ‘m’ widgets using ESN, the processor 202 may be configured toconcatenate word content of the ‘n’ Web pages in the visitor's ongoingjourney in the exact sequence as they appeared in the Web pages to forma concatenated input sequence ω _(journey) as depicted below:

ω _(journey)=(w ₁ ¹ , w ₂ ¹ , . . . w _(p1) ¹ |w ₁ ² , w ₂ ² , . . . w_(p2) ² |w ₁ ^(n) , w ₂ ^(n) , . . . w _(pn) ^(n))

The word sequence of the ‘m’ widgets may be described by input sequencesω _(widget) ^(i) for i=1, 2, 3, . . . m, where ω _(widget) ^(i) may berepresented as follows:

ω _(widget) ^(i)=(ω₁ ^(i), ω₂ ^(i), . . . ω_(β1) ^(i))

Where ω₁ ^(i), ω₂ ^(i), . . . ω_(β1) ^(i) corresponds to word sequenceof the i^(th) widget.

Further, the processor 202 is configured to generate a global worddictionary including all the words from the visited Web pages in theonline visitor's journey as well as the ‘m’ widgets in the widget bank.The global word dictionary may be represented as follows:

ω _(global)=ω _(journey)∪ω _(widget) ¹∪ω _(widget) ². . . ∪ω _(widget)^(m)

From the global word dictionary ω _(global), the processor 202 isconfigured to determine mean of the word lengths i.e. μ_(global), and astandard deviation of the word lengths, i.e., σ_(global).

Further, the processor 202 is configured to create an ESN reservoir

_(journey) based on the mean μ_(global) and the standard deviationσ_(global). In an illustrative example, the ESN reservoir

_(journey) includes a plurality of sigmoidal neurons (for example, 100sigmoidal neurons).

In an embodiment, the neurons in the ESN reservoir

_(journey) are configured to receive inputs from η_(IP) input units,where η_(IP) input units are computed (by the processor 202) using themean μ_(global) and the standard deviation σ_(global) as depicted inequation (3):

η_(IP)=μ_(global)+2σ_(global)   (3)

Furthermore, in an embodiment, the processor 202 is configured to create‘m’ identical clones of the ESN reservoir

_(journey), where each clone is configured to represent at least onewidget from among the ‘m’ widgets in the widget bank. Moreover, theclones (denoted by (

₁,

₁, . . .

_(m))) may be identical in terms of synaptic weights as well as neuronparameters.

In an example scenario, if a length of a number of characters in a wordis less that η_(IP), then no input is provided to remaining neurons inthat point of time. Similarly, if the number of characters in the wordis longer or greater than η_(IP), then only the first η_(IP) charactersare sent as input to the ESN reservoir

_(journey).

Accordingly, a concatenated input sequence of the input text ω_(journey), is provided to the ESN reservoir

_(journey), one word at a time and a final dynamic state of the ESNreservoir

_(journey) represented by χ_(journey) is recorded. Similarly, wordsequence of input widgets ω _(widget) ^(i) are provided to the ESNreservoir

_(journey) and a final state of the ESN reservoir

_(journey), represented by χ_(i) ^(final) for i=1, 2, 3 . . . m, isrecorded.

Based on the final states obtained from the input text (or text from theonline visitor's journey, more particularly text from the Web pagesvisited by the online visitor) χ_(journey) ^(final) and the final stateof the ESN reservoir χ_(journey) ^(final) a correlation between eachwidget and content of Web pages is computed by the processor 202 of theapparatus 200.

The computation of correlation between each widget and content of Webpages is further explained with reference to FIG. 7.

FIG. 7 shows an example representation 700 for illustrating aprovisioning a portion of Web page content as an input to an ESNreservoir 702, in accordance with an embodiment of the invention.

The ESN reservoir 702 is exemplarily depicted to include a plurality ofneurons, such as neurons 704, 706, and the like. The neurons aresparsely connected (i.e. some neurons are connected to each, whereassome neurons are not connected with each other) with each other viasynapses, such as a synapse 708, connecting the neurons 704 and 708.

As explained above, a global dictionary is first configured byaggregating all words in the Web pages visited so far and the wordsincluded in the widgets of the widget bank. The mean and standarddeviation of the words lengths are then computed (based on equation (3)provided above) to determine a number of input units (say ‘n’) to beprovided to the ESN reservoir 702 at each input entry. In FIG. 7, thechosen value of ‘n’ is depicted to be six (as depicted by block 710associated with the text ‘input neuron index’). Accordingly, at eachtime step, input to six neurons in the ESN reservoir 702 may beprovided.

In an illustrative example, a portion of Web page content to be providedas input to the ESN reservoir 702 includes the text ‘A QUICK BROWN FOXJUMPED OVER A LAZY DOG’. The sequence of words starting from the firstword until the last word in the input text (i.e., starting from firstletter ‘A’ to the last word ‘DOG’) is provided to the ESN reservoir 702with each word provided at one time instance, i.e., at one time step.The time steps for providing input of a word (such as ‘A’, ‘Quick’,‘Brown’ etc.) is depicted exemplarily by block 712 associated with thetext ‘time step’). The example representation 700 further depicts ablock 714 showing an ordering of words input sequence arranged as pertime step for provisioning words to the ESN reservoir 702.

Further, as explained above, if a length of a number of characters in aword is less than ‘n’ then no input is provided to remaining neurons inthat point of time. Similarly, if the number of characters in the wordis longer or greater than ‘n’, then only the first ‘n’ characters aresent as input to the ESN reservoir 702. For example, the first word inthe input sequence is ‘A’. Because a number of characters in the firstword is less than six, then no input is provided to remaining neurons(i.e. five neurons) at that point in time, as exemplarily depicted bycrosses in the block 714. Similarly, the other words in the inputsequence are associated with crosses (or no input to neurons) if thenumber of characters is less than six.

Each word in the input sequence is provided to the ESN reservoir 702,such that at the most six neurons receive a character input at each timestep. A state of the ESN reservoir 702 subsequent to the provisioning ofthe input sequence is then compared with a final state of similar ESNreservoirs (i.e. reservoirs identical in terms of synaptic weights aswell as neuron parameters) upon provisioning of content of respectivewidgets. A correlation score may then be computed by comparing thelinear readouts of the final state of the ESN reservoir 702 with thefinal states of the ESN reservoirs corresponding to each widget. Thewidgets may then be rank ordered and top ‘k’ widgets with highestcorrelation values selected by the processor 202 and provisioned to theonline visitor. Moreover, the top ‘k’ widgets are provisioned to theonline visitor at an appropriate point in time in the ongoing customerjourney.

It is noted that a key benefit of using an ESN reservoir for computingcorrelation scores is that an arbitrary length journey can be compressedinto a fixed number of output dimensions (text from long journeys, aswell as short text corresponding to widgets may all be compressed intopre-determined dimension, such as 100 dimensions for example, whichcorresponds to the final state of the 100 neuron reservoir). This isespecially beneficial when comparing two arbitrary length sequencesagainst one another.

A method for dynamically selecting content for an online visitor isexplained with reference to FIG. 8.

FIG. 8 is a flow diagram of an example method 800 for dynamicallyselecting content for online visitors, in accordance with an embodimentof the invention. The method 800 depicted in the flow diagram may beexecuted by, for example, the apparatus 200 explained with reference toFIGS. 1 to 7. Operations of the flowchart, and combinations of operationin the flowchart, may be implemented by, for example, hardware,firmware, a processor, circuitry, and/or a different device associatedwith the execution of software that includes one or more computerprogram instructions. The operations of the method 800 are describedherein with help of the apparatus 200. For example, one or moreoperations corresponding to the method 800 may be executed by aprocessor, such as the processor 202 of the apparatus 200. Although theone or more operations are explained herein to be executed by theprocessor alone, it is understood that the processor is associated witha memory, such as the memory 204 of the apparatus 200, which isconfigured to store machine executable instructions for facilitating theexecution of the one or more operations. The operations of the method800 can be described and/or practiced by using an apparatus other thanthe apparatus 200. The method 800 starts at operation 802.

At operation 802 of the method 800, information related to activity ofan online visitor on an enterprise interaction channel is received by aprocessor, such as the processor 202 explained with reference to FIG. 2.The activity corresponds to an ongoing journey of the online visitor onthe enterprise interaction channel. Some non-exhaustive examples of anenterprise interaction channel may include a Web channel, a socialchannel, a native application channel, and the like. In an illustrativeexample, an online visitor may visit a Website, i.e. the Web channelassociated with an enterprise for learning about products and/orservices associated with the enterprise. In such a scenario, thereceived information related to activity of the online visitor mayinclude information, such as information related to a current Web pagethat the online visitor is accessing, previous Web pages visited, timespent on a Web page, search terms used, and the like. In anotherillustrative example, an online visitor may visit a native mobileapplication associated with an enterprise for performing a task, such aspaying a bill amount, making a purchase transaction, etc. In such ascenario, the received information related to activity of the onlinevisitor may include information, such as information related to a menuoption that the online visitor is accessing, links clicked, imagesaccessed, search terms used, and the like.

At operation 804 of the method 800, channel data related to the activityof the online visitor is identified by the processor using the receivedinformation. For example, if the received information corresponds toonline visitor's Website activity information, then the Web pagesvisited by the online visitor during the online journey may beidentified. In such a scenario, the channel data identified for such avisitor journey may correspond to data (for example, textual data)corresponding to Web page content of the Web pages visited by the onlinevisitor during the ongoing journey on the enterprise Website. In anotherillustrative example, if the received information corresponds to onlinevisitor's activity on social media forum, then UIs corresponding to thesocial media forum visited by the online visitor during the onlinejourney may be identified. In such a scenario, the channel dataidentified for such a visitor journey may correspond to data (forexample, textual data) corresponding to UIs visited by the onlinevisitor during the ongoing journey on the enterprise social media forum.

It is noted that channel data may include one or more data segments. Forexample, if the channel data corresponds to overall content of all Webpages visited by the online visitor, then each data segment maycorrespond to content related to one Web page visited by the onlinevisitor. Similarly, if the channel data corresponds to overall textualcontent of UIs of the native mobile application accessed by the onlinevisitor, then each data segment may correspond to textual content of oneUI from among the several UIs accessed by the online visitor.

In an embodiment, upon receiving information related to an onlinevisitor's journey, all possible content (also interchangeably referredto herein as ‘content pieces’) that may be offered to the online visitormay be retrieved from a storage location, such as the memory 204. Forexample, several content pieces such as widgets (or pop-ups) includingtextual content related to advertisements, promotional offers, discountcoupons, news snippets, frequently asked questions (FAQs), and the like,may be retrieved.

At operation 806 of the method 800, a correlation score is computed bythe processor for each content piece from among the plurality of contentpieces. The correlation score for each content piece is computed usingthe channel data. The computation of the correlation score for eachcontent piece generates a plurality of correlation scores. Thecorrelation score is indicative of a measure of correlation betweencontent corresponding to a respective content piece and contentcorresponding to a data segment. The correlation score for each contentpiece may be computed as a cosine similarity score (such as thepage-wise cosine similarity score), a cumulative cosine similarity scoreor using ESN reservoirs as explained with reference to FIGS. 4 to 7 andis not explained again herein.

At operation 808 of the method 800, the plurality of content pieces arerank-ordered by the processor by sorting the plurality of correlationscores. For example, the correlation scores may be sorted in adescending order and rank-ordered from top to bottom. At operation 810of the method 800, a display of at least one content piece from amongthe plurality of content pieces is effected by the processor during theongoing journey of the online visitor on the enterprise interactionchannel. The display of the at least one content piece is effected basedon the rank-ordering of the plurality of content pieces. For example,the top ‘k’ widgets may be selected based on the rank associated witheach widget. In an illustrative example, the value of ‘k’ may be chosento be one. Accordingly, the top ranking widget may be displayed to thevisitor on the Website. For example, for an online visitor visiting aWebsite to purchase a laptop, a widget including static content relatedto an equated monthly installment (EMI) scheme for purchasing laptopsmay be displayed to the online visitor on the Website. Similarly, awidget including advertisement content related to a promotional offer orcontent related to a Web-link showcasing laptop brand comparisons,laptop model reviews, and the like, may also be displayed to the onlinevisitor. In some embodiments, for an online visitor wishing to resolve aquery, a chat widget offering a chat interaction with a human agent or avirtual agent, or alternatively, frequently asked questions (FAQ) pagecontent may be displayed to the online visitor. It is understood thatthe value of ‘k’ for selecting k top widgets is described herein to be‘one’ for illustration purposes and it is understood that the value of kmay be chosen to be any number greater than one, and the displayprovided to the online visitor may be appended/modified, accordingly.

Although the method 800 is explained with reference to a single onlinevisitor visiting a Website, the method 800 may be extended todynamically select content for a plurality of online visitors visitingvarious interaction channels, such as Websites, native mobileapplications, social media etc. Furthermore, the dynamically selectedcontent may be chosen from among varied types of content pieces, such asthose related to advertisements, FAQ, new snippets, chat assistancepop-ups, etc.

Another method for dynamically selecting content for an online visitoris explained with reference to FIG. 9.

FIG. 9 is a flow diagram of an example method 900 for dynamicallyselecting content for online visitors, in accordance with anotherembodiment of the invention. The method 900 depicted in the flow diagrammay be executed by, for example, the apparatus 200 explained withreference to FIGS. 2 to 7. Operations of the flowchart, and combinationsof operation in the flowchart, may be implemented by, for example,hardware, firmware, a processor, circuitry, and/or a different deviceassociated with the execution of software that includes one or morecomputer program instructions. The method 900 starts at operation 902.

At operation 902 of the method 900, information related to activity ofan online visitor on an enterprise Website is received by a processor,such as the processor 202 of the apparatus 200 explained with referenceto FIG. 2. The activity corresponds to an ongoing journey of the onlinevisitor on the enterprise Website is received.

At operation 904 of the method 900, Website data related to the activityof the online visitor using the received information is identified bythe processor. The Website data includes data corresponding to one ormore Web pages visited by the online visitor during the ongoing journey.

At operation 906 of the method 900, a correlation score is computed bythe processor for each widget from among the plurality of widgets. Thecorrelation score is computed using the Website data. The computation ofthe correlation score for each widget generates a plurality ofcorrelation scores.

At operation 908 of the method 900, the plurality of widgets arerank-ordered by the processor by sorting the plurality of correlationscores.

At operation 910 of the method 900, a display of at least one widgetfrom among the plurality of widgets is effected by the processor duringthe ongoing journey of the online visitor on the enterprise Website. Thedisplay of the at least one widget is effected based on therank-ordering of the plurality of widgets.

Various embodiments disclosed herein provide numerous advantages. Thetechniques disclosed herein suggest techniques for dynamically selectingcontent that is customized to an individual online visitor's currentbehavior or preference. Moreover, dynamic selection of relevant contentcan be extended to scenarios, where sufficient historical visitorinteraction data is absent. This is especially useful for first timeonline visitors to enterprise interaction channels as appropriatecontent may be provided to the visitors without the need to access anyhistorical data for predicting intent of the visitors. To that effect,three unsupervised learning based approaches are disclosed that can beused to dynamically determine relevant content based on an individualvisitor's journey across an enterprise interaction channel. Morespecifically, the individual pieces of content (for example, widgets,advertisements etc.) are ranked based on their correlation with thecontent of the interaction channel at any arbitrary point in time in theonline visitor's journey and the most relevant content-pieces (forexample, higher ranking content pieces) are provisioned to the onlinevisitor. Such provisioning of the relevant content to the online visitorat appropriate point in time in the ongoing visitor journey provides apersonalized experience to the online visitor, thereby improving anonline browsing experience of the online visitor and increasing chancesof a sale.

Although the invention has been described with reference to specificexemplary embodiments, it is noted that various modifications andchanges may be made to these embodiments without departing from thebroad spirit and scope of the invention. For example, the variousoperations, blocks, etc., described herein may be enabled and operatedusing hardware circuitry (for example, complementary metal oxidesemiconductor (CMOS) based logic circuitry), firmware, software and/orany combination of hardware, firmware, and/or software (for example,embodied in a machine-readable medium). For example, the apparatuses andmethods may be embodied using transistors, logic gates, and electricalcircuits (for example, application specific integrated circuit (ASIC)circuitry, and/or in Digital Signal Processor (DSP) circuitry).

Particularly, the apparatus 200, the processor 202, the memory 204, theI/O module 206 and the communication interface 208 may be enabled usingsoftware and/or using transistors, logic gates, and electrical circuits(for example, integrated circuit circuitry such as ASIC circuitry).Various embodiments of the present technology may include one or morecomputer programs stored or otherwise embodied on a computer-readablemedium, wherein the computer programs are configured to cause aprocessor or computer to perform one or more operations (for example,operations explained herein with reference to FIGS. 8 and 9). Acomputer-readable medium storing, embodying, or encoded with a computerprogram, or similar language, may be embodied as a tangible data storagedevice storing one or more software programs that are configured tocause a processor or a computer to perform one or more operations. Suchoperations may be, for example, any of the steps or operations describedherein. In some embodiments, the computer programs may be stored andprovided to a computer using any type of non-transitory computerreadable media. Non-transitory computer readable media include any typeof tangible storage media. Examples of non-transitory computer readablemedia include magnetic storage media (such as floppy disks, magnetictapes, hard disk drives, etc.), optical magnetic storage media (e.g.magneto-optical disks), CD-ROM (compact disc read only memory), CD-R(compact disc recordable), CD-R/W (compact disc rewritable), DVD(Digital Versatile Disc), BD (BLU-RAY® Disc), and semiconductor memories(such as mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flashmemory, RAM (random access memory), etc.). Additionally, a tangible datastorage device may be embodied as one or more volatile memory devices,one or more non-volatile memory devices, and/or a combination of one ormore volatile memory devices and non-volatile memory devices. In someembodiments, the computer programs may be provided to a computer usingany type of transitory computer readable media. Examples of transitorycomputer readable media include electric signals, optical signals, andelectromagnetic waves. Transitory computer readable media can providethe program to a computer via a wired communication line (e.g. electricwires, and optical fibers) or a wireless communication line.

Various embodiments of the invention, as discussed above, may bepracticed with steps and/or operations in a different order, and/or withhardware elements in configurations, which are different than thosewhich, are disclosed. Therefore, although the technology has beendescribed based upon these exemplary embodiments, it is noted thatcertain modifications, variations, and alternative constructions may beapparent and well within the spirit and scope of the invention.

Although various exemplary embodiments of the invention are describedherein in a language specific to structural features and/ormethodological acts, the subject matter defined in the appended claimsis not necessarily limited to the specific features or acts describedabove. Rather, the specific features and acts described above aredisclosed as exemplary forms of implementing the claims.

1. A computer-implemented method, comprising: receiving, by a processor,information related to activity of an online visitor on an enterpriseinteraction channel for an ongoing journey of the online visitor on theenterprise interaction channel; identifying, by the processor, channeldata related to the activity of the online visitor using the receivedinformation, the channel data comprising one or more data segments;computing, by the processor, a correlation score for each content piecefrom among a plurality of content pieces using the channel data, thecomputation of the correlation score for each content piece generating aplurality of correlation scores, wherein each content piece may beprovided to the online visitor during the ongoing journey on theenterprise interaction channel; rank-ordering, by the processor, theplurality of content pieces by sorting the plurality of correlationscores; and based on the rank-ordering of the plurality of contentpieces, effecting, by the processor, display of at least one contentpiece from among the plurality of content pieces during the ongoingjourney of the online visitor on the enterprise interaction channel. 2.The method of claim 1, further comprising: computing by the processor,for the each content piece, a cosine similarity score corresponding toeach data segment from among the one or more data segments to generateone or more cosine similarity scores for the each content piece, whereineach cosine similarity score indicates a measure of correlation betweencontent corresponding to a respective content piece and contentcorresponding to a data segment from among the one or more datasegments.
 3. The method of claim 2, wherein computing the cosinesimilarity score comprises: identifying a list of elements common to thecontent of the respective content piece and the content of the datasegment; computing a first vector based on a number of occurrences ofeach element from among the list of elements in the content of the datasegment; and computing a second vector based on a number of occurrencesof the each element in the content of the respective content piece;wherein the each cosine similarity score is computed, at least in part,by determining a cosine component of an angle between the first vectorand the second vector.
 4. The method of claim 3, further comprising: forthe each content piece, computing, by the processor, an average value ofthe respective one or more cosine similarity scores, the average valueof the respective one or more cosine similarity scores configuring thecorrelation score for the each content piece.
 5. The method of claim 1,further comprising: computing by the processor, for the each contentpiece, a cumulative cosine similarity score indicative of a measure ofcorrelation between content corresponding to a respective content pieceand cumulative content corresponding to the one or more data segments,wherein the respective cumulative cosine similarity score computed forthe each content piece configures the correlation score for the eachcontent piece.
 6. The method of claim 5, wherein computing thecumulative cosine similarity score for a content piece comprises:identifying a list of elements common to content of the content pieceand the cumulative content corresponding to the one or more datasegments; computing a cumulative term vector based on a number ofoccurrences of each element from among the list of elements in thecumulative content; and computing a second vector based on a number ofoccurrences of the each element in the content of the content piece,wherein the cumulative cosine similarity score is computed, at least inpart, by determining a cosine component of an angle between thecumulative term vector and the second vector.
 7. The method of claim 1,further comprising: computing the correlation score for the each contentpiece using a reservoir computing framework.
 8. The method of claim 7,wherein the reservoir computing framework corresponds to an Echo statenetwork (ESN).
 9. The method of claim 8, further comprising: generating,by the processor, a primary ESN reservoir comprising a plurality ofneurons, wherein one or more neurons from among the plurality of neuronsare interconnected randomly or connected to each other based on apredefined probability distribution function; providing, by theprocessor, a first input comprising a sequence of elements configuringcontent corresponding to one or more data segments; and recording, bythe processor, a state of the primary ESN reservoir subsequent toprovisioning of the first input to the primary ESN reservoir, whereinthe correlation score for the each content piece is computed, at leastin part, based on the state of the primary ESN reservoir.
 10. The methodof claim 9, further comprising: generating, by the processor, aplurality of secondary ESN reservoirs, each secondary ESN reservoir fromamong the plurality of secondary ESN reservoirs configured to be a cloneof the primary ESN reservoir; providing to each secondary ESN reservoir,by the processor, a respective second input comprising a sequence ofelements configuring content corresponding to one content piece fromamong the plurality of content pieces; recording, by the processor, afinal state of each secondary ESN reservoir subsequent to provisioningof the respective second input; and comparing, by the processor, thefinal state of each secondary ESN reservoir to the state of the primaryESN reservoir, wherein the correlation score for the each content pieceis computed based on the comparison of the final state of the respectivesecondary ESN reservoir with the state of the primary ESN reservoir. 11.The method of claim 10, further comprising: generating, by theprocessor, a set comprising a plurality of elements, the plurality ofelements comprising elements corresponding to the one or more datasegments and elements corresponding to the plurality of content pieces;computing, by the processor, a standard deviation value and a mean valuefrom respective lengths of the plurality of elements; and determining,by the processor, a length of each element in the first input and in thesecond input based on the standard deviation value and the mean value.12. The method of claim 1, further comprising: displaying a highestranking content piece from among the plurality of content pieces to theonline visitor during the ongoing journey on the enterprise interactionchannel.
 13. The method of claim 1, wherein the plurality of contentpieces comprises one or more widgets displaying content related to atleast one of an advertisement, a promotional offer, a discount offer, anoffer for agent assistance, a news story, and a frequently askedquestion (FAQ).
 14. The method of claim 1, wherein the enterpriseinteraction channel corresponds to a Website and the each data segmentcorresponds to a Web page visited by the online visitor during theongoing journey on the enterprise interaction channel.
 15. An apparatus,comprising: at least one processor; and a memory having stored thereinmachine executable instructions, that when executed by the at least oneprocessor, cause the apparatus to: receive information related toactivity of an online visitor on an enterprise interaction channel foran ongoing journey of the online visitor on the enterprise interactionchannel; identify channel data related to the activity of the onlinevisitor using the received information, the channel data comprising oneor more data segments; compute a correlation score for each contentpiece from among a plurality of content pieces using the channel data,the computation of the correlation score for the each content piecegenerating a plurality of correlation scores, wherein each content piecemay be provided to the online visitor during the ongoing journey on theenterprise interaction channel; rank-order the plurality of contentpieces by sorting the plurality of correlation scores; and based on therank-ordering of the plurality of content pieces, effect display of atleast one content piece from among the plurality of content piecesduring the ongoing journey of the online visitor on the enterpriseinteraction channel.
 16. The apparatus of claim 15, wherein theapparatus is further caused to: compute for the each content piece, acosine similarity score corresponding to each data segment from amongthe one or more data segments to generate one or more cosine similarityscores for the each content piece, wherein each cosine similarity scoreindicates a measure of correlation between content corresponding to arespective content piece and content corresponding to a data segmentfrom among the one or more data segments.
 17. The apparatus of claim 16,wherein for computing the each cosine similarity score, the apparatus iscaused to: identify a list of elements common to the content of therespective content piece and the content of the data segment; compute afirst vector based on a number of occurrences of each element from amongthe list of elements in the content of the data segment; and compute asecond vector based on a number of occurrences of the each element inthe content of the respective content piece, wherein the each cosinesimilarity score is computed, at least in part, by determining a cosinecomponent of an angle between the first vector and the second vector.18. The apparatus of claim 17, wherein the apparatus is further causedto: for the each content piece, compute an average value of therespective one or more cosine similarity scores, the average value ofthe respective one or more cosine similarity scores configuring thecorrelation score for the each content piece.
 19. The apparatus of claim15, wherein the apparatus is further caused to: compute, for the eachcontent piece, a cumulative cosine similarity score indicative of ameasure of correlation between content corresponding to a respectivecontent piece and cumulative content corresponding to the one or moredata segments, wherein the respective cumulative cosine similarity scorecomputed for the each content piece configures the correlation score forthe each content piece.
 20. The apparatus of claim 19, wherein forcomputing the cumulative cosine similarity score for a content piece,the apparatus is caused to: identify a list of elements common tocontent of the content piece and the cumulative content corresponding tothe one or more data segments; compute a cumulative term vector based ona number of occurrences of each element from among the list of elementsin the cumulative content; and compute a second vector based on a numberof occurrences of the each element in the content of the content piece,wherein the cumulative cosine similarity score is computed, at least inpart, by determining a cosine component of an angle between thecumulative term vector and the second vector.
 21. The apparatus of claim15, wherein the correlation score for the each content piece is computedusing an Echo state network (ESN) framework.
 22. The apparatus of claim21, wherein the apparatus is further caused to: generate a primary ESNreservoir comprising a plurality of neurons, one or more neurons fromamong the plurality of neurons interconnected randomly or connected toeach other based on a predefined probability distribution function;provide a first input comprising a sequence of elements configuringcontent corresponding to one or more data segments; and record a stateof the primary ESN reservoir subsequent to provisioning of the firstinput to the primary ESN reservoir, wherein the correlation score forthe each content piece is computed, at least in part, based on the stateof the primary ESN reservoir.
 23. The apparatus of claim 22, wherein theapparatus is further caused to: generate a plurality of secondary ESNreservoirs, each secondary ESN reservoir from among the plurality ofsecondary ESN reservoirs configured to be a clone of the primary ESNreservoir; provide to each secondary ESN reservoir, a respective secondinput comprising a sequence of elements configuring contentcorresponding to one content piece from among the plurality of contentpieces; record a final state of each secondary ESN reservoir subsequentto provisioning of the respective second input; and compare the finalstate of each secondary ESN reservoir to the state of the primary ESNreservoir, wherein the correlation score for the each content piece iscomputed based on the comparison of the final state of the respectivesecondary ESN reservoir with the state of the primary ESN reservoir. 24.The apparatus of claim 23, wherein the apparatus is further caused to:generate a set comprising a plurality of elements, the plurality ofelements comprising elements corresponding to the one or more datasegments and elements corresponding to the plurality of content pieces;compute a standard deviation value and a mean value from respectivelengths of the plurality of elements; and determine a length of eachelement in the first input and in the second input based on the standarddeviation value and the mean value.
 25. The apparatus of claim 15,wherein the apparatus is further caused to: display a highest rankingcontent piece from among the plurality of content pieces to the onlinevisitor during the ongoing journey on the enterprise interactionchannel.
 26. An apparatus, comprising: at least one communicationinterface configured to receive information related to activity of anonline visitor on an enterprise Website for an ongoing journey of theonline visitor on the enterprise Website; at least one processor; and amemory having stored therein machine executable instructions, that whenexecuted by the at least one processor, cause the apparatus to: identifyWebsite data related to the activity of the online visitor using thereceived information, the Website data comprising data corresponding toone or more Web pages visited by the online visitor during the ongoingjourney; compute a correlation score for each widget from among aplurality of widgets using the Website data, the computation of thecorrelation score for the each widget generating a plurality ofcorrelation scores, wherein each widget may be provided to the onlinevisitor during the ongoing journey on the enterprise Website,;rank-order the plurality of widgets by sorting the plurality ofcorrelation scores; and based on the rank-ordering of the plurality ofwidgets, effect display of at least one widget from among the pluralityof widgets during the ongoing journey of the online visitor on theenterprise Website.
 27. The apparatus of claim 26, wherein the apparatusis further caused to: compute for the each widget, a cosine similarityscore corresponding to data for each Web page from among the one or moreWeb pages to generate one or more cosine similarity scores for the eachwidget, wherein each cosine similarity score indicates a measure ofcorrelation between content corresponding to a respective widget andcontent corresponding to a Web page from among the one or more Webpages.
 28. The apparatus of claim 27, wherein for computing the eachcosine similarity score, the apparatus is caused to: identify a list ofwords common to the content of the respective widget and the content ofthe Web page; compute a first vector based on a number of occurrences ofeach word from among the list of words in the content of the Web page;and compute a second vector based on a number of occurrences of the eachword in the content of the respective widget, wherein the each cosinesimilarity score is computed, at least in part, by determining a cosinecomponent of an angle between the first vector and the second vector.29. The apparatus of claim 28, wherein the apparatus is further causedto: for the each widget, compute an average value of the respective oneor more cosine similarity scores, the average value of the respectiveone or more cosine similarity scores configuring the correlation scorefor the each widget.
 30. The apparatus of claim 26, wherein theapparatus is further caused to: compute, for the each widget, acumulative cosine similarity score indicative of a measure ofcorrelation between content corresponding to a respective widget andcumulative content corresponding to the one or more Web pages, whereinthe respective cumulative cosine similarity score computed for the eachwidget configures the correlation score for the each widget.
 31. Theapparatus of claim 30, wherein for computing the cumulative cosinesimilarity score for a content piece, the apparatus is caused to:identify a list of words common to content of the widget and thecumulative content corresponding to the one or more Web pages; compute acumulative term vector based on a number of occurrences of each wordfrom among the list of words in the cumulative content; compute a secondvector based on a number of occurrences of the each word in the contentof the widget, wherein the cumulative cosine similarity score iscomputed, at least in part, by determining a cosine component of anangle between the cumulative term vector and the second vector.
 32. Theapparatus of claim 26, wherein the apparatus is further caused to:generate a primary echo state network (ESN) reservoir comprising aplurality of neurons, one or more neurons from among the plurality ofneurons interconnected randomly or connected to each other based on apredefined probability distribution function; provide a first inputcomprising a sequence of words configuring content corresponding to oneor more Web pages; and record a state of the primary ESN reservoirsubsequent to provisioning of the first input to the primary ESNreservoir, wherein the correlation score for the each widget iscomputed, at least in part, based on the state of the primary ESNreservoir.
 33. The apparatus of claim 32, wherein the apparatus isfurther caused to: generate a plurality of secondary ESN reservoirs,each secondary ESN reservoir from among the plurality of secondary ESNreservoirs configured to be a clone of the primary ESN reservoir;provide to each secondary ESN reservoir, a respective second inputcomprising a sequence of words configuring content corresponding to onewidget from among the plurality of widgets; record a final state of eachsecondary ESN reservoir subsequent to provisioning of the respectivesecond input; and compare the final state of each secondary ESNreservoir to the state of the primary ESN reservoir, wherein thecorrelation score for the each widget is computed based on thecomparison of the final state of the respective secondary ESN reservoirwith the state of the primary ESN reservoir.
 34. The apparatus of claim33, wherein the apparatus is further caused to: generate a setcomprising a plurality of words, the plurality of words comprising wordscorresponding to the one or more Web pages and words corresponding tothe plurality of widgets; compute a standard deviation value and a meanvalue from respective lengths of the plurality of words; and determine alength of each word in the first input and in the second input based onthe standard deviation value and the mean value.
 35. The apparatus ofclaim 26, wherein the apparatus is further caused to: display a highestranking widget from among the plurality of widgets to the online visitorduring the ongoing journey on the enterprise Website.
 36. The apparatusof claim 26, wherein least one widget from among the plurality ofwidgets is configured to display advertisement content or contentoffering agent chat assistance to the online visitor.